Contour-based segmentation and coding for depth map compression

Jäger, Fabian

doi:10.1109/vcip.2011.6115989

Cited by 33 publications

(15 citation statements)

References 5 publications

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“…Some approaches explicitly encode the position of the most significant depth edges (see for e.g. [15]). The main depth contours are signaled and the in-between texture is encoded with piecewise-linear functions.…”

Section: Related Workmentioning

confidence: 99%

Depth map compression via 3D region-based representation

Duch

Morros

Ruiz-Hidalgo

2016

Multimed Tools Appl

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In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an efficient encoding while preserving the 3D characteristics of the scene. The 3D planes open up the possibility to code multiview images with a unique representation.

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Section: Related Workmentioning

confidence: 99%

Depth map compression via 3D region-based representation

Duch

Morros

Ruiz-Hidalgo

2016

Multimed Tools Appl

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“…The precision of the boundary information was seen as an important factor which needs to be improved in [28]. Piecewise linear approximations were used for selecting the important contours in [29], while encoding the contours was realized by JBIG2.…”

Section: B Related Workmentioning

confidence: 99%

Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

Tăbuş

Schiopu

Astola

2013

IEEE Trans. on Image Process.

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Abstract-This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression.

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“…The early good performers were based on platelets [9] and region based coding [10]. Especially related with the method proposed in this letter are the encoding with piecewise linear approximations of the contours [11] and the lossy methods using region merging and splitting [12]. Scalable lossy coding was introduced based on a scalable description of the geometry and subband coding [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Patch-Based Conditional Context Coding of Stereo Disparity Images

Tăbuş

2014

IEEE Signal Process. Lett.

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Abstract-This letter proposes a method for lossless coding the left disparity image, L, from a stereo disparity image pair (L,R), conditional on the right disparity image, R, by keeping track of the transformation of the constant patches from R to L. The disparities in R are used for predicting the disparities in L, and the locations of the pixels where the prediction is erroneous are encoded in a first stage, conditional on the patch-labels of R image, allowing the decoder to already reconstruct with certainty some elements of the L image, e.g., the disparity values at certain pixels and parts of the contours of left image patches. Second, the contours of the patches in L image that are still unknown after first stage are conditionally encoded using a mixed conditioning context: the usual causal current context from the contours of L and a noncausal context extracted from the contours in the correctly estimated part of L obtained in the first stage. The depth values in the patches of L image are finally encoded, if they are not already known from the prediction stage. The new algorithm, dubbed conditional crack-edge region value (C-CERV), is shown to perform significantly better than the non-conditional coding method CERV and than another existing conditional coding method, over the Middlebury corpus. C-CERV is shown to reach lossless compression ratios of 100-250 times for those images that have a high precision of the disparity map.

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Contour-based segmentation and coding for depth map compression

Cited by 33 publications

References 5 publications

Depth map compression via 3D region-based representation

Depth map compression via 3D region-based representation

Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

Patch-Based Conditional Context Coding of Stereo Disparity Images

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